Publications

Underpowered trials risk inaccurate results. Recruitment to stroke rehabilitation randomised controlled trials (RCTs) is often a challenge. Statistical simulations offer an important opportunity to explore the adequacy of sample sizes in the context of specific outcome measures. We aimed to examine and compare the adequacy of stroke rehabilitation RCT sample sizes using the Barthel Index (BI) or modified Rankin Scale (mRS) as primary outcomes.We conducted computer simulations using typical experimental event rates (EER) and control event rates (CER) based on individual participant data (IPD) from stroke rehabilitation RCTs. Event rates are the proportion of participants who experienced clinically relevant improvements in the RCT experimental and control groups. We examined minimum sample size requirements and estimated the number of participants required to achieve a number needed to treat within clinically acceptable boundaries for the BI and mRS.We secured 2350 IPD (18 RCTs). For a 90% chance of statistical accuracy on the BI a rehabilitation RCT would require 273 participants per randomised group. Accurate interpretation of effect sizes would require 1000s of participants per group. Simulations for the mRS were not possible as a clinically relevant improvement was not detected when using this outcome measure.Stroke rehabilitation RCTs with large sample sizes are required for accurate interpretation of effect sizes based on the BI. The mRS lacked sensitivity to detect change and thus may be unsuitable as a primary outcome in stroke rehabilitation trials.Copyright © 2021 Elsevier Inc. All rights reserved.

JTD Keywords:  , barthel index, design, increasing value, modified rankin scale, randomised controlled trials, recruitment, reducing waste, reliability, sample size calculations, simulations, stroke rehabilitation, Adult, Article, Barthel index, Calculation, Computer simulation, Controlled study, Effect size, Female, Human, Human experiment, Major clinical study, Male, Modified rankin scale, Numbers needed to treat, Outcome assessment, Randomised controlled trials, Randomized controlled trial, Randomized controlled-trials, Rankin scale, Recruitment, Rehabilitation, Sample size, Sample size calculations, Simulations, Stroke rehabilitation

The unique ability to identify one's own body and experience it as one's own is fundamental in goal-oriented behavior and survival. However, the mechanisms underlying the so-called body ownership are yet not fully understood. Evidence based on Rubber Hand Illusion (RHI) paradigms has demonstrated that body ownership is a product of reception and integration of self and externally generated multisensory information, feedforward and feedback processing of sensorimotor signals, and prior knowledge about the body. Crucially, however, these designs commonly involve the processing of proximal modalities while the contribution of distal sensory signals to the experience of ownership remains elusive. Here we propose that, like any robust percept, body ownership depends on the integration and prediction across all sensory modalities, including distal sensory signals pertaining to the environment. To test our hypothesis, we created an embodied goal-oriented Virtual Air Hockey Task, in which participants were to hit a virtual puck into a goal. In two conditions, we manipulated the congruency of distal multisensory cues (auditory and visual) while preserving proximal and action-driven signals entirely predictable. Compared to a fully congruent condition, our results revealed a significant decrease on three dimensions of ownership evaluation when distal signals were incongruent, including the subjective report as well as physiological and kinematic responses to an unexpected threat. Together, these findings support the notion that the way we represent our body is contingent upon all the sensory stimuli, including distal and action-independent signals. The present data extend the current framework of body ownership and may also find applications in rehabilitation scenarios.

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal–hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems. © 2021 Elsevier Ltd

JTD Keywords: computational model, dentate gyrus, error backpropagation, granule cells, grid cells, hippocampus, inhibition, input, neural-networks, neurons, transformation, Artificial intelligence, Artificial neural network, Back propagation, Backpropagation, Brain, Cognitive systems, Counter current, Error back-propagation, Error backpropagation, Errors, Expressing interneurons, Hippocampal complex, Hippocampus, Human experiment, Input and outputs, Learning, Mammal, Mammalian hippocampus, Mammals, Neural networks, Nonhuman, Review, Self-supervised learning